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Adopting electric end-use technologies instead of fossil-fueled alternatives, known as electrification, is an important economy-wide decarbonization strategy that also reduces criteria pollutant emissions and improves air quality. In this study, we evaluate CO2 and air quality co-benefits of electrification scenarios by linking a detailed energy systems model and a full-form photochemical air quality model in the United States. We find that electrification can substantially lower CO2 and improve air quality and that decarbonization policy can amplify these trends, which yield immediate and localized benefits. In particular, transport electrification can improve ozone and fine particulate matter (PM2.5), though the magnitude of changes varies regionally. However, growing activity from non-energy-related PM2.5 sources—such as fugitive dust and agricultural emissions—can offset electrification benefits, suggesting that additional measures beyond CO2 policy and electrification are needed to meet air quality goals. We illustrate how commonly used marginal emissions approaches systematically underestimate reductions from electrification. Electrification is a decarbonization strategy that has the potential to reduce pollutant emissions and improve air quality. Here the authors evaluate CO2 and air quality co-benefits of electrification scenarios in the United States and find that electrification can substantially lower CO2 and improve ozone and fine particulate matter regionally. [ABSTRACT FROM AUTHOR]

This study represents an inter- comparison of four regional-scale air quality simulations, focused on understanding similarities and differences in the simulated impact of ozone lateral boundary conditions (LBCs) on the ground-level ozone predictions across the U.S. The chemically inert tracers were implemented in the simulations as a diagnostic tool to understand the similarities and differences between models at process level. For all simulations, three chemically inert tracers (BC1 BC2 and BC3) are used to track the impact of ozone specified at different altitudes along the lateral boundaries of the modeling domain encompassing the contiguous U.S. The altitude ranges specified for BC1, BC2, and BC3 broadly represent the planetary boundary layer (PBL), free troposphere, and upper troposphere-lower stratosphere, respectively. The four simulations, namely WRF/CMAQ, WRF/CAMx, WRF/DEHM and COSMO-CLM/CMAQ, can have considerable differences in the simulated inert tracers at surface, indicating their different estimates in the impact of lateral boundary on surface ozone within the U.S. due to the physical processes alone in chemical transport models. WRF/CMAQ is used as a base case, and the differences between WRF/CMAQ and the other three models are examined, respectively. The model pair of COSMO-CLM/CMAQ and WRF/CMAQ shows the smallest differences in inert tracers, with the difference in BCT (sum of BC1, BC2 and BC3) peak in winter to be 1.6 ppb averaged across all sites. The model pair of WRF/DEHM and WRF/CMAQ shows the largest differences, with difference in BCT peak in summer to be 8.1 ppb averaged across all sites. Furthermore, the model differences in inert tracers are discussed with respect to the physical processes that inert tracers undergo. It is found that the process of vertical turbulent mixing between the PBL and the free troposphere is the main cause of the model differences in the simulated inert tracers, especially the relative contributions of BC1 and BC2 to the total inert tracers, in most seasons and regions of the U.S., although the processes of sub-grid cloud mixing and dry deposition can also be important drivers for specific regions and seasons. [ABSTRACT FROM AUTHOR]

Inter-continental ozone (O3) transport extends the geographic range of O3 air pollution impacts and makes local air pollution management more difficult. Phase 3 of the Air Quality Modeling Evaluation International Initiative (AQMEII-3) is examining the contribution of inter-continental transport to regional air quality by applying regional scale atmospheric models jointly with global models. We investigate methods for tracing O3 from global models within regional models. The CAMx photochemical grid model was used to track contributions from boundary condition (BC) O3 over a North America modeling domain for calendar year 2010 using a built-in tracer module called RTCMC. RTCMC can track BC contributions using chemically reactive tracers and also using inert tracers in which deposition is the only sink for O3. Lack of O3 destruction chemistry in the inert tracer approach leads to over estimation biases that can exceed 10 ppb. The flexibility of RTCMC also allows tracking O3 contributions made by groups of vertical BC layers. The largest BC contributions to seasonal average daily maximum 8-hour averages (MDA8) of O3 over the US are found to be from the mid-troposphere with small contributions from the upper troposphere-lower stratosphere. Contributions from the lower troposphere are shown to not penetrate very far inland. Higher contributions in the Western than the Eastern US, reaching an average of 57 ppb in Denver for the 30 days with highest MDA8 O3 in 2010, present a significant challenge to air quality management approaches based solely on local or US-wide emission reductions. The substantial BC contribution to MDA8 O3 in the Intermountain West means regional models are particularly sensitive to any biases and errors in the BCs. A sensitivity simulation with reduced BC O3 in response to 20 % lower emissions in Asia found a near linear relationship between the BC O3 changes and surface O3 changes in the Western US in all seasons and across the US in fall and winter. However, the surface O3 decreases are small: below 1 ppb in spring and below 0.5 ppb in other seasons. [ABSTRACT FROM AUTHOR]

Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale. [ABSTRACT FROM AUTHOR]

Abstract: We conducted four source apportionment simulations using the Community Multiscale Air Quality (CMAQ) photochemical grid modelling system in order to investigate and assess the relative importance of specific emissions sources and/or geographic regions on ambient ozone and PM air quality in the Alberta Oil Sands Region (AOSR). Local point and area sources, medium range transport of mobile sources and biogenic emissions were examined. The elimination of emissions from local stationary point sources reduced 8-h ozone concentrations and greatly reduces the PM2.5 concentrations in AOSR; similar results are seen in the local area source zero-out simulations but with less ozone and PM2.5 reductions than seen in the local point source zero-out run. Although the mobile source medium-range transport reduced 8-h ozone concentrations in the urban plume from Edmonton that results in some ozone and PM2.5 concentration reductions in the southern part of the AOSR, it has little effect on the elevated ozone and PM2.5 concentrations in the oil sands development area of the AOSR where the highest ozone and PM2.5 concentrations are estimated. The elimination of all anthropogenic emissions in Alberta so that only biogenic emissions remained resulted in large reductions in 8-h ozone concentrations in the AOSR, with the highest remaining ozone in the 50–52ppb range occurring south of Fort McMurray; very low PM2.5 concentrations are also estimated across Alberta, including the AOSR, when anthropogenic emissions are eliminated in Alberta. [Copyright &y& Elsevier]

Abstract: The Community Multiscale Air Quality (CMAQ) and the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling systems were used to simulate emissions and air quality in North Eastern Alberta where a rapid rise in oil sands development has caused air quality concerns over the last decade. The models were run on a 36/12/4 km domain for the four month period of May through August 2002. A model performance evaluation was conducted by comparing the CMAQ model estimates against ambient air quality measurements. In the Alberta oil sands region, the model tended to achieve or nearly achieve ozone model performance goals, albeit with an underestimation bias. The magnitudes of the observed PM2.5 concentrations were matched by the modeling system, except when the observed PM2.5 concentrations were influenced by emissions from forest fires in which case the model underestimated the observed PM2.5 concentrations. The CMAQ-estimated 4th highest daily maximum 8-hour ozone concentrations in the oil sands region were below the 65 ppb Canada Wide Standard (CWS) as well as the 58 ppb Alberta Management Plan Trigger Level. The highest estimated ozone concentrations occurred near the oil sands development area just north of Fort McMurray with values approaching, but below, the 58 ppb Management Plan Trigger Level; estimated ozone concentrations are much lower in the farther northern portions of the oil sands region. The acute (i.e., maximum 3-day value) SUM60 vegetative ozone exposure metric was mostly less than 100 ppb h, which is below the threshold of concern for crops. However, just north of Fort McMurray there were small areas where the acute SUM60 metric exceeded the 500–700 ppb h threshold of concern for crops with maximum values in plumes from sources in the oil sands mine area of ∼900 ppb h. The maximum chronic (three-month average) SUM60 ozone exposure metric was below the thresholds of concern. The CMAQ-estimated maximum 98th percentile 24-hour average PM2.5 concentration occurs in the mine area north of Fort McMurray and was below the CWS threshold (30 μg m−3), however it was above the Alberta Management Plan Trigger Level (20 μg m−3). The estimated 98th percentile 24- hour average PM2.5 concentrations were mostly in the 2–6 μg m−3 range with regions of higher PM2.5 concentrations predicted just north of Fort McMurray in the oil sands development region. [Copyright &y& Elsevier]